Papers

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Viewing 1-10 of 758 papers
  • Exploring Team-Sourced Hyperlinks to Address Navigation Challenges for Low-Vision Readers of Scientific Papers

    Soya Park, Jonathan Bragg, Michael Chang, Kevin Larson, Danielle BraggCSCW2022 Reading academic papers is a fundamental part of higher education and research, but navigating these information-dense texts can be challenging. In particular, low-vision readers using magnification encounter additional barriers to quickly skimming and…
  • FeedLens: Polymorphic Lenses for Personalizing Exploratory Search over Knowledge Graphs

    Harmanpreet Kaur, Doug Downey, Amanpreet Singh, Evie (Yu-Yen) Cheng, Daniel S. Weld, Jonathan BraggUIST2022 The vast scale and open-ended nature of knowledge graphs (KGs) make exploratory search over them cognitively demanding for users. We introduce a new technique, polymorphic lenses , that improves exploratory search over a KG by obtaining new leverage from the…
  • Threddy: An Interactive System for Personalized Thread-based Exploration and Organization of Scientific Literature

    Hyeonsu B. Kang, Joseph Chee Chang, Yongsung Kim, Aniket KitturUIST2022 Reviewing the literature to understand relevant threads of past work is a critical part of research and vehicle for learning. However, as the scientific literature grows the challenges for users to find and make sense of the many different threads of research…
  • Webly Supervised Concept Expansion for General Purpose Vision Models

    Amita Kamath, Christopher Clark, Tanmay Gupta, Eric Kolve, Derek Hoiem, Aniruddha KembhaviECCV2022 General purpose vision (GPV) systems [25] are models that are designed to solve a wide array of visual tasks without requiring architectural changes. Today, GPVs primarily learn both skills and concepts from large fully supervised datasets. Scaling GPVs to…
  • A Dataset of Alt Texts from HCI Publications

    Sanjana Chintalapati, Jonathan Bragg, Lucy Lu WangASSETS2022 Figures in scientifc publications contain important information and results, and alt text is needed for blind and low vision readers to engage with their content. We conduct a study to characterize the semantic content of alt text in HCI publications based on…
  • The Abduction of Sherlock Holmes: A Dataset for Visual Abductive Reasoning

    Jack Hessel, Jena D. Hwang, Jae Sung Park, Rowan Zellers, Chandra Bhagavatula, Anna Rohrbach, Kate Saenko, Yejin ChoiECCV2022 Humans have remarkable capacity to reason abductively and hypothesize about what lies beyond the literal content of an image. By identifying concrete visual clues scattered throughout a scene, we almost can’t help but draw probable inferences beyond the…
  • Benchmarking Progress to Infant-Level Physical Reasoning in AI

    currently under blind reviewunder submission2022 To what extent do modern AI systems comprehend the physical world? We introduce the open-access Infant-Level Physical Reasoning Benchmark ( InfLevel ) to gain insight into this question. We evaluate ten neural-network architectures developed for video…
  • Pace v0.1: A python-based performance-portable implementation of the FV3 dynamical core

    Johann Dahm, Eddie Davis, Florian Deconinck, Oliver Elbert, Rhea George, Jeremy McGibbon, Tobias Wicky, Elynn Wu, Christopher Kung, Tal Ben-Nun, Lucas Harris, Linus Groner, and Oliver FuhrerEGUsphere2022 Progress in leveraging current and emerging high-performance computing infrastructures using traditional weather and climate models has been slow. This has become known more broadly as the software productivity gap. With the end of Moore's Law driving forward…
  • Do Androids Laugh at Electric Sheep? Humor"Understanding"Benchmarks from The New Yorker Caption Contest

    Jack Hessel, Ana Marasović, Jena D. Hwang, Lillian Lee, Jeff Da, Rowan Zellers, Robert Mankoff, Yejin ChoiarXiv2022 We challenge AI models to “demonstrate un-derstanding” of the sophisticated multimodal humor of The New Yorker Caption Contest. Concretely, we develop three carefully cir-cumscribed tasks for which it suffices (but is not necessary) to grasp potentially…
  • Correcting a 200 km Resolution Climate Model in Multiple Climates by Machine Learning From 25 km Resolution Simulations

    S. Clark, Noah Brenowitz, B. Henn, Anna Kwa, J. McGibbon, W. Perkins, Oliver Watt‐Meyer, C. Bretherton, L. HarrisJournal of Advances in Modeling Earth Systems2022 Bretherton et al. (2022, https://doi.org/10.1029/2021MS002794) demonstrated a successful approach for using machine learning (ML) to help a coarse‐resolution global atmosphere model with real geography (a ∼200 km version of NOAA's FV3GFS) evolve more like a…